A model‐based map of ground temperatures for the permafrost regions of Canada
Bibliographic record
Abstract
Abstract A functional model of the permafrost‐climate system is applied at national scale, to produce a map of near‐surface ground temperatures in the permafrost regions of Canada. The TTOP model links the temperature at the top of permafrost (TTOP) to the climate through seasonal surface transfer functions and subsurface thermal properties. The parameters in the model were compiled at national scale for Canada, although the topographic effects of the Western Cordillera were not incorporated into the analysis. The objective of the study was accomplished by implementing the TTOP model within a Geographical Information System. The TTOP map is evaluated against the published Ground Temperature Map of Canada. The published map shows ground temperatures according to a scale of temperature classes, so TTOP values were categorized into the same classes. Across the permafrost regions of Canada, 72.1% of the area is in the same class in both maps, while 27.7% differs by one temperature class. Only 0.2% of the area differs by two temperature classes. The results suggest that the TTOP model can provide a rational and functional basis for relating near‐surface permafrost temperature and climate at national and regional scales. The model could be applied to the assessment of climate change impacts on the magnitude and distribution of permafrost temperatures. Copyright © 2001 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".